Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models by Theng, Lau Wei

“…This paper presents an image classification method on 4 types of salak (salak pondoh, salak gading, salak sideempuan and salak affinis) using a Convolutional Neural Network (CNN), VGG16 and ResNet50. …”
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An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method by Heba Soliman Zaina (16904787)

“…Our proposed method was tested using the MPRAGE structural MRI dataset from Alzheimer Disease Neuro Imaging Initiative (ADNI), and it outperformed other techniques used in the literature review. …”

Fresh Fruit Bunch Ripeness Classification Methods: A Review by Jin Yu Goh (22046393)

“…The present review scrutinizes the following non-destructive ripeness classification methods: spectroscopy, inductive sensing, thermal imaging, light detection and ranging, laser-light backscattering imaging, and computer vision. …”

Lung cancer medical images classification using hybrid CNN-SVM by Abdulrazak Yahya Saleh (18520026)

“…This paper presents an image classification method based on the hybrid Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM). …”

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images by Sami Elzeiny (16891521)

“…By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. …”

Drone/Bird Classification Based on Features of Tracks Trajectories by Kengeskanov, Maksat

“…Standard Machine Learning methods such as SVM and Random Forest are used for learning this classification. …”
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Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification by Lulwa Ahmed (16869936)

“…Promising results are obtained on both applications resulting in comparable results against the best-case scenario where each sample is manually analyzed and annotated (Baseline 1), and improvement of 3.1% and 4% with best methods respectively over the training sets with irrelevant images on natural disaster and waste classification datasets (Baseline 2).…”

An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge by Zaulkernan, Imran

“…Camera traps deployed in remote locations provide an effective method for ecologists to monitor and study wildlife in a non-invasive way. …”
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Binary Classification of 3D Small-Scale Medical Images Using Video Vision Transformers by Abi Younes, Simon

“…The VesselMNIST3d dataset binary classification experiment was implemented by treating the 3D image as video where the third dimension represents the number of frames. …”
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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance by Krishnamoorthy Natarajan (22047464)

Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images by Rehan Raza (17019105)

“…In the present article, we proposed a novel transfer learning-based predictor called, Lung-EffNet for lung cancer classification. Lung-EffNet is built based on the architecture of EfficientNet and further modified by adding top layers in the classification head of the model. …”

Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning by Abdo, Ahmed

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A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images by Sonam Aggarwal (16904763)

“…However, only a few methods for automated prediction of protein localization have been developed, and they mostly concentrate on single-label classification. …”

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images by Hassaan Malik (10486121)

“…The scalogram method is used to convert the cough sounds into an image. …”

Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala (22330876)

“…In this study, we introduce an innovative method for the multi-classification of breast cancer histopathological images utilizing Bidirectional Recurrent Neural Networks (BRNN). …”

A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification by Mohamed Sultan Mohamed Ali (17317003)

“…<p>Hyperspectral imaging (HSI) plays a pivotal role in remote sensing, enabling precise material identification through spectral data across many bands. …”

Feature modeling using polynomial classifiers and stepwise regression by Shanableh, Tamer

“…Experimental results illustrate the effectiveness of the proposed dimensionality reduction technique in comparison to published methods.…”
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Hybrid Neural Networks for Precise Hydronephrosis Classification Using Deep Learning by Abdus Salam (1918387)

“…</p><h3 dir="ltr">Methods</h3><p dir="ltr">A dataset of 1731 renal ultrasound images, annotated by four experienced urologists, was used for model training and evaluation. …”

Automated detection of posterior urethral valves in voiding cystourethrography images: A novel AI-Based pipeline for enhanced diagnosis and classification by Saidul Kabir (15302407)

“…The main objective was to detect presence of PUV based on urethral ratio calculated automatically from segmented urethra region. </p><h3>Methods </h3><p dir="ltr">A total of 181 VCUG images were evaluated by 9 clinicians to determine presence of PUV. …”

Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation by Raveendra Pilli (21633287)

“…Features from MRI images are extracted using 3-D-CNN and fed into the wavelet KRR-RVFL network for brain age classification and prediction. …”